Automated video looping with progressive dynamism

ABSTRACT

Various technologies described herein pertain to generating a video loop. An input video can be received, where the input video includes values at pixels over a time range. An optimization can be performed to determine a respective input time interval within the time range of the input video for each pixel from the pixels in the input video. The respective input time interval for a particular pixel can include a per-pixel loop period and a per-pixel start time of a loop at the particular pixel within the time range from the input video. Moreover, an output video can be created based upon the values at the pixels over the respective input time intervals for the pixels in the input video.

BACKGROUND

Visual imagery commonly can be classified as either a static image(e.g., photograph, painting, etc.) or dynamic imagery (e.g., video,animation, etc.). A static image captures a single instant in time. Forinstance, a static photograph often derives its power by what is impliedbeyond its spatial and temporal boundaries (e.g., outside the frame andin moments before and after the photograph was taken). Typically, aviewer's imagination can fill in what is left out of the static image(e.g., spatially and/or temporally). In contrast, video loses some ofthat power; yet, by being dynamic, video can provide an unfoldingtemporal narrative through time.

Differing types of short videos can be created from an input video.Examples of the short videos include cinemagraphs and cliplets, whichselectively freeze, play, and loop video regions to achieve compellingeffects. The contrasting juxtaposition of looping elements against astill background can help grab the attention of a viewer. For instance,cinemagraphs can commonly combine static scenes with small repeatingmovements (e.g., a hair wisp blowing in the wind); thus, some motion andnarrative can be captured in a cinemagraph. In a cinemagraph, thedynamic element is commonly looping in a sequence of frames.

Various techniques are conventionally employed to create video loops.For example, some approaches define video textures by locating pairs ofsimilar video frames to create a sparse transition graph. A stochastictraversal of this graph can generate non-repeating video; however,finding compatible frames may be difficult for scenes with manyindependently moving elements when employing such techniques. Othertraditional approaches for creating video loops synthesize videos usinga Markov Random Field (MRF) model. Such approaches can successivelymerge video patches offset in space and/or time, and determine anoptimal merging scene using a binary graph cut. Introducing constraintscan allow for creation of video loops with a specified global period.Other conventional techniques attempt to create panoramic video texturesfrom a panning video sequence. Accordingly, a user can select a staticbackground layer image and can draw masks to identify dynamic regions.For each region, a natural periodicity can be automatically determined.Then a 3D MRF model can be solved using a multi-label graph cut on a 3Dgrid. Still other techniques attempt to create panoramic stereo videotextures by blending the overlapping video in the space-time volume.

Various approaches for interactive authoring of cinemagraphs have beendeveloped. For example, regions of motion in a video can beautomatically isolated. Moreover, a user can select which regions tomake looping and which reference frame to use for each region. Loopingcan be achieved by finding matching frames or regions. Some conventionaltechniques for creating cinemagraphs can selectively stabilize motionsin video. Accordingly, a user can sketch differing types of strokes toindicate regions to be made static, immobilized, or fully dynamic, wherethe strokes can be propagated across video frames using optical flow.The video can further be warped for stabilization and a 3D MRF problemcan be solved to seamlessly merge the video with static content. Otherrecent techniques provide a set of idioms (e.g., static, play, loop andmirror loop) to allow a user to combine several spatiotemporal segmentsfrom a source video. These segments can be stabilized and compositedtogether to emphasize scene elements or to form a narrative.

SUMMARY

Described herein are various technologies that pertain to generating avideo loop. An input video can be received, where the input videoincludes values at pixels over a time range. An optimization can beperformed to determine a respective input time interval within the timerange of the input video for each pixel from the pixels in the inputvideo. The respective input time interval for a particular pixel caninclude a per-pixel loop period and a per-pixel start time of a loop atthe particular pixel within the time range from the input video.According to an example, a two-stage optimization algorithm can beemployed to determine the respective input time intervals.Alternatively, by way of another example, a single-stage optimizationalgorithm can be employed to determine the respective input timeintervals. Moreover, an output video can be created based upon thevalues at the pixels over the respective input time intervals for thepixels in the input video.

According to various embodiments, a progressive video loop spectrum forthe input video can be created based upon the optimization, wherein theprogressive video loop spectrum can encode a segmentation (e.g., anested segmentation, a disparate type of segmentation, etc.) of thepixels in the input video into independently looping spatial regions.The progressive video loop spectrum can include video loops with varyinglevels of dynamism, ranging from a static image to an animated loop witha maximum level of dynamism. In accordance with various embodiments, theinput video can be remapped to form a compressed input video. Thecompressed input video can include a portion of the input video. Theportion of the input video, for example, can be a portion accessed by aloop having a maximum level of dynamism in the progressive video loopspectrum.

In accordance with various embodiments, a selection of a level ofdynamism for the output video can be received. Moreover, the outputvideo can be created based upon the values from the input video and theselection of the level of dynamism for the output video. The level ofdynamism in the output video can be controlled based upon the selectionby causing spatial regions of the output video to respectively be eitherstatic or looping. Further, the output video can be rendered on adisplay screen of a device.

The above summary presents a simplified summary in order to provide abasic understanding of some aspects of the systems and/or methodsdiscussed herein. This summary is not an extensive overview of thesystems and/or methods discussed herein. It is not intended to identifykey/critical elements or to delineate the scope of such systems and/ormethods. Its sole purpose is to present some concepts in a simplifiedform as a prelude to the more detailed description that is presentedlater.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a functional block diagram of an exemplary systemthat generates a video loop from an input video.

FIG. 2 illustrates an exemplary input video V (x, t) and a correspondingexemplary output video L (x, t).

FIG. 3 illustrates an exemplary time-mapping from an input video to anoutput video.

FIG. 4 illustrates a functional block diagram of an exemplary systemthat generates a video loop from an input video using a two-stageoptimization algorithm.

FIG. 5 illustrates a functional block diagram of an exemplary systemthat generates a progressive video loop spectrum from an input video.

FIG. 6 illustrates exemplary loops of a progressive video loop spectrumgenerated by the system of FIG. 5.

FIG. 7 illustrates an exemplary graphical representation of constructionof the progressive video loop spectrum implemented by the system of FIG.5.

FIG. 8 illustrates a functional block diagram of an exemplary systemthat controls rendering of an output video.

FIG. 9 illustrates a functional block diagram of an exemplary systemthat compresses an input video.

FIG. 10 illustrates an exemplary input video and an exemplary compressedinput video.

FIG. 11 is a flow diagram that illustrates an exemplary methodology forgenerating a video loop.

FIG. 12 is a flow diagram that illustrates an exemplary methodology forcompressing an input video.

FIG. 13 is a flow diagram that illustrates an exemplary methodology fordisplaying an output video on a display screen of a device.

FIG. 14 illustrates an exemplary computing device.

DETAILED DESCRIPTION

Various technologies pertaining to generating a spectrum of video loopswith varying levels of dynamism from an input video, where the spectrumof video loops ranges from a static image to an animated loop with amaximum level of dynamism, are now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of one or more aspects. It may be evident,however, that such aspect(s) may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to facilitate describing one or moreaspects. Further, it is to be understood that functionality that isdescribed as being carried out by certain system components may beperformed by multiple components. Similarly, for instance, a componentmay be configured to perform functionality that is described as beingcarried out by multiple components.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom the context, the phrase “X employs A or B” is intended to mean anyof the natural inclusive permutations. That is, the phrase “X employs Aor B” is satisfied by any of the following instances: X employs A; Xemploys B; or X employs both A and B. In addition, the articles “a” and“an” as used in this application and the appended claims shouldgenerally be construed to mean “one or more” unless specified otherwiseor clear from the context to be directed to a singular form.

As set forth herein, a representation that captures a spectrum oflooping videos with varying levels of dynamism can be created from aninput video. The representation is referred to herein as a progressivevideo loop spectrum. Video loops in the progressive video loop spectrumrange from a static loop to an animated loop that has a maximum level ofdynamism. Intermediate loops between the static loop and the loop havingthe maximum level of dynamism in the progressive video loop spectrumhave levels of dynamism between that of the static loop and the loophaving the maximum level of dynamism. When creating an output video fromthe input video and the progressive video loop spectrum, a desiredamount of scene liveliness can be interactively adjusted (e.g., using aslider, through local selection of a spatial region, etc.). The outputvideo created as described herein can be utilized for variousapplications such as for background images or slideshows, where a levelof activity may depend on personal taste or mood. Moreover, therepresentation may segment a scene into independently looping spatialregions, enabling interactive local adjustment over dynamism. For alandscape scene, for example, this control may correspond to selectiveanimation and de-animation of grass motion, water ripples, and swayingtrees. The input video can be converted to looping content by employingan optimization in which a per-pixel loop period for each pixel of theinput video can be automatically determined. Further, a per-pixel starttime for each pixel of the input video can be automatically determinedby performing the optimization (e.g., the optimization cansimultaneously solve for the per-pixel loop period and the per-pixelstart time for each pixel of the input video). Moreover, the resultingsegmentation of static and dynamic scene regions can be compactlyencoded.

Referring now to the drawings, FIG. 1 illustrates a system 100 thatgenerates a video loop from an input video 102. The system 100 includesa reception component 104 that receives the input video 102, where theinput video 102 includes values at pixels over a time range. The inputvideo 102 can be denoted as a three-dimensional (3D) volume V (x, t),with two-dimensional (2D) pixel location x and frame time t. The 2Dpixel location x is also referred to herein as the pixel x.

The system 100 automates forming looping content from the input video102. Certain motions in a scene included in the input video 102 can berendered in an output video 112. It is contemplated that such motionscan be stochastic or semi-periodic such as, for example, swayinggrasses, swinging branches, rippling puddles, and pulsing lights. Thesemoving elements in a scene often have different loop periods;accordingly, the system 100 can automatically identify a respectiveper-pixel loop period for each pixel of the input video 102 as well as arespective per-pixel start time for each pixel of the input video 102.At a given pixel, a combination of a per-pixel loop period and aper-pixel start time can define an input time interval in the inputvideo 102. A length of the input time interval is the per-pixel loopperiod, and a first frame of the input time interval is the per-pixelstart time. Moreover, it is contemplated that some moving objects in theinput video 102 can be static (e.g., frozen) in the output video 112

Conventional techniques for forming loops typically rely on useridentification of spatial regions of the scene that are looping and userspecification of a loop period for each of the identified spatialregions. Such conventional techniques also commonly rely on useridentification of spatial regions of the scene that are static. Incontrast to traditional approaches, the system 100 formulates video loopcreation as an optimization in which a per-pixel loop period can bedetermined for each pixel of the input video 102. Moreover, it iscontemplated that the per-pixel loop period of one or more of the pixelsof the input video 102 may be unity, whereby a pixel becomes static.Therefore, the optimization can automatically segment a scene intoregions with naturally occurring periods, as well as regions that arestatic.

Further, looping content can be parameterized to preserve phasecoherence, which can cause the optimization to be more tractable. Foreach pixel, there can be one degree of freedom available to temporallyshift a video loop (e.g., the repeating time interval identified fromthe input video 102 using the per-pixel loop period and the per-pixelstart time) in the output video 112. Thus, different delays can beintroduced at each pixel, where a delay for a given pixel influenceswhen the given pixel begins a loop in the output video 112. These delayscan be set so as to preserve phase coherence, which can enhancespatiotemporal consistency. Accordingly, if two adjacent pixels areassigned the same per-pixel loop period and have respective input timeintervals with non-zero overlap, then the pixel values within the timeoverlap can concurrently appear for both pixels in the output video 112.By way of illustration, if pixel C and pixel D have a common per-pixelloop period, and pixel C has a start frame that is 2 frames earlier thanpixel D, then the loop at pixel D in the output video 112 can be shiftedby 2 frames relative to the loop at pixel C such that content of thepixel C and the pixel D appears to be synchronized.

The system 100 can be at least part of a dedicated interactive tool thatallows the output video 112 to be produced from the input video 102, forexample. According to another example, the system 100 can be at leastpart of a set of dedicated interactive tools, which can include adedicated interactive tool for forming a video loop from the input video102 and a disparate dedicated interactive tool for producing the outputvideo 112 from the formed video loop. By way of another example, it iscontemplated that the system 100 can be included in a device thatcaptures the input video 102; thus, the system 100 can be configured forexecution by a processor of the device that captures the input video102. Following this example, a camera of a smartphone can capture theinput video 102, and a user can employ the smartphone to create theoutput video 112 using the system 100 (e.g., executed by a processor ofthe smartphone that captured the input video 102). Pursuant to a furtherexample, a portion of the system 100 can be included in a device thatcaptures the input video 102 (e.g., configured for execution by aprocessor of the device that captures the input video 102) and aremainder of the system 100 can be included in a disparate device (e.g.,configured for execution by a processor of the disparate device);following this example, the portion of the system 100 included in thedevice that captures the input video 102 can form a video loop, whilethe remainder of the system 100 included in the disparate device cancreate the output video 112 from the formed video loop.

The reception component 104 can receive the input video 102 fromsubstantially any source. For example, the reception component 104 canreceive the input video 102 from a camera that captures the input video102. Pursuant to another example, a camera that captures the input video102 can include the reception component 104. According to anotherexample, the reception component 104 can receive the input video 102from a data repository that retains the input video 102. It is to beappreciated, however, that the claimed subject matter is not limited tothe foregoing examples.

Many types of devices, such as smartphones, cameras, tablet computers,laptop computers, mobile gaming consoles, and the like, can capture theinput video 102. For instance, it is to be appreciated that such typesof devices can capture high-definition video as well as photographs.Moreover, with increased parallel processing, the gap in resolutionbetween these two media is narrowing. Thus, it may become morecommonplace to archive short bursts of video rather than still frames.Accordingly, looping content can be automatically formed from the shortbursts of captured video using the system 100.

The input video 102 received by the reception component 104 may havepreviously been stabilized (e.g., prior to receipt by the receptioncomponent 104), for example. According to another example, the inputvideo 102 can be stabilized subsequent to being received by thereception component 104 (e.g., the reception component 104 can stabilizethe input video 102, a stabilization component can stabilize the inputvide 102, etc.). Stabilization of the input video 102 can be performedautomatically or with user guidance.

The system 100 further includes a loop construction component 106 thatcan cause an optimizer component 108 to perform an optimization todetermine a respective input time interval within the time range of theinput video 102 for each pixel from the pixels in the input video 102. Arespective input time interval for a particular pixel can include aper-pixel loop period and a per-pixel start time of a loop at theparticular pixel within the time range from the input video 102. Forexample, the loop construction component 106 can cause the optimizercomponent 108 to perform the optimization to determine the respectiveinput time intervals within the time range of the input video 102 forthe pixels that optimize an objective function.

Moreover, the system 100 includes a viewer component 110 that can createthe output video 112 based upon the values at the pixels over therespective input time intervals for the pixels in the input video 102.The viewer component 110 can generate the output video 112 based uponthe video loop created by the loop construction component 106. Theoutput video 112 can include looping content and/or static content. Theoutput video 112 can be denoted as a 3D volume L (x, t), with the 2Dpixel location x and frame time t. Moreover, the viewer component 110can cause the output video 112 to be rendered on a display screen of adevice.

The system 100 attempts to maintain spatiotemporal consistency in theoutput video 112 (e.g., a loop can avoid undesirable spatial seams ortemporal pops that can occur when content of the output video 112 is notlocally consistent with the input video 102). Due to stabilization ofthe input video 102, the output video 112 can be formed by the viewercomponent 110 retrieving, for each pixel of the output video 112,content associated with the same pixel in the input video 102. Thecontent retrieved from the input video 102 and included in the outputvideo 112 by the viewer component 110 can be either static or looping.More particularly, the content can be represented as a temporal interval[s_(x), s_(x)+p_(x)) from the input video 102, where s_(x) is aper-pixel start time of a loop for a pixel x and p_(x) is a per-pixelloop period for the pixel x. The per-pixel start time s_(x) and theper-pixel loop period p_(x) can be expressed in units of frames. Astatic pixel thus corresponds to the case p_(x)=1.

Turning to FIG. 2, illustrated is an exemplary input video 200, V (x,t), and a corresponding exemplary output video 202, L (x, t). Input timeintervals can be determined for each pixel in the input video 200 (e.g.,by the loop construction component 106 of FIG. 1). As shown, pixelsincluded in a spatial region 204 of the input video 200 each have aper-pixel start time of s_(x) and a per-pixel loop period of p_(x).Further, as depicted, pixels included in a spatial region 206 of theinput video 200 and pixels included in a spatial region 208 of the inputvideo 200 each have a common per-pixel loop period; however, a per-pixelstart time of the pixels included in the spatial region 206 of the inputvideo 200 differs from a per-pixel start time of the pixels included inthe spatial region 208 of the input video 200. Moreover, pixels includedin a spatial region 210 of the input video 200 are static (e.g., a unityper-pixel loop period).

Values from the respective input time intervals for the pixels from theinput video 200 can be time-mapped to the output video 202. For example,the input time interval from the input video 200 for the pixels includedin the spatial region 206 can be looped in the output video 202 for thepixels included in the spatial region 206. Also, as depicted, staticvalues for the pixels included in the spatial region 210 from thespecified time of the input video 200 can be maintained for the pixelsincluded in the spatial region 210 over a time range of the output video202.

The time-mapping function utilized to map the input time intervals fromthe input video 200 to the output video 202 can preserve phasedifferences between differing spatial regions, which can assistmaintaining spatial consistency across adjacent pixels in differingspatial regions with a common per-pixel loop period and differingper-pixel start times. Thus, an offset between the input time intervalfor the pixels in the spatial region 206 and the input time interval forthe pixels in the spatial region 208 from the input video 200 can bemaintained in the output video 202 to provide synchronization.

Again, reference is made to FIG. 1. The viewer component 110 cantime-map a respective input time interval for a particular pixel in theinput video 102 to the output video 112 utilizing a modulo-basedtime-mapping function. An output of the modulo-based time-mappingfunction for the particular pixel can be based on the per-pixel loopperiod and the per-pixel start time of the loop at the particular pixelfrom the input video 102. Accordingly, a relation between the inputvideo 102 and the output video 112 can be defined as:

L(x,t)=V(x,φ(x,t)),t≧0.

In the foregoing, φ (x, t) is the time-mapping function set forth asfollows:

$\begin{matrix}{{\varphi \left( {x,t} \right)} = {s_{x} - \left( {s_{x}{mod}\mspace{11mu} p_{x}} \right) + \left( {t\mspace{11mu} {mod}\mspace{11mu} p_{x}} \right) + \left\{ \begin{matrix}0 & {{{{if}\mspace{14mu} \left( {t\mspace{11mu} {mod}\mspace{11mu} p_{x}} \right)} \geq \left( {s_{x}\mspace{11mu} {mod}\mspace{11mu} p_{x}} \right)},} \\p_{x} & {{otherwise}.}\end{matrix} \right.}} & (1)\end{matrix}$

Due to the above modulo arithmetic of the time-mapping function, if twoadjacent pixels are looping with the same period in the input video 102,then the viewer component 110 can cause such adjacent pixels to bein-phase in the output video 112 (e.g., in an output loop).

FIG. 3 illustrates an exemplary time-mapping from an input video 300 toan output video 302. In the depicted example of FIG. 3, pixel x andpixel z are spatially adjacent. The pixels x and z have the sameper-pixel loop period, and thus, p_(x)=p_(z). Further, a start time forthe pixel x, s_(x), differs from a start time for the pixel z, s_(z).

Content from the input time interval [s_(x), s_(x)+p_(x)) of the inputvideo 300 can be retrieved for the pixel x and content from the inputtime interval [s_(z), s_(z)+p_(z)) of the input video 300 can beretrieved for the pixel z. Although the start times s_(x) and s_(z)differ, the input time intervals can have significant overlap asillustrated by the arrows between the input time intervals in the inputvideo 300. Since the adjacent pixels x and z have the same loop periodand similar start times, the in-phase time-mapping function of Equation1 above can automatically preserve spatiotemporal consistency over asignificant portion of the output timeline shown in FIG. 3 for theoutput video 302 (represented by the arrows). The time-mapping functioncan wrap the respective input time intervals for the pixel x and thepixel z in the output timeline to maintain adjacency within the temporaloverlap, and thus, can automatically preserve spatial consistency.

Solving for start times can encourage phase coherence to be maintainedbetween adjacent pixels. Moreover, loops within the input video 102 canhave regions that loop in-phase with a common optimized period, but withstaggered per-pixel start times for differing regions. In contrast todetermining start times for pixels, some conventional approaches solvefor time offsets between output and input videos.

While many of the examples set forth herein pertain to time-mappingwhere loops from an input video move forward in time in an output video,other types of time-mappings are intended to fall within the scope ofthe hereto appended claims. For instance, time-mappings such as mirrorloops, reverse loops, or reverse mirror loops can be employed, and thus,optimization for such other types of time-mappings can be performed.

Reference is again made to FIG. 1. As set forth herein, the loopconstruction component 106 can cause the optimizer component 108 toperform the optimization to determine the respective input timeintervals within the time range of the input video 102 for the pixelsthat optimize an objective function. The objective function can includea spatial consistency term, a temporal consistency term, and a dynamismterm that penalizes assignment of static loops at the pixels based upontemporal variances of neighborhoods of the pixels in the input video102. Video loop construction can be formulated as an MRF problem.Accordingly, the per-pixel start times s={s_(x)} and the per-pixel loopperiods p={p_(x)} that minimize the following objective function can beidentified:

E(s,p)=E _(consistency)(s,p)+E _(static)(s,p)

In the foregoing objective function, the first term can encourage pixelneighborhoods in the video loop to be consistent both spatially andtemporally with those in the input video 102. Moreover, the second termin the above noted objective function can penalize assignment of staticloop pixels except in regions of the input video 102 that are static. Incontrast to conventional approaches, the MRF graph can be defined over a2D spatial domain rather than a full 3D video volume. Also, in contrastto conventional approaches, the set of unknowns can include a per-pixelloop period at each pixel.

According to an example, the loop construction component 106 can causethe optimizer component 108 to solve the MRF optimization using amulti-label graph cut algorithm, where the set of pixel labels is theouter product of candidate start times {s} and periods {p}. Followingthis example, the multi-label graph cut algorithm can be a single-stageoptimization algorithm that simultaneously solves for per-pixel loopperiods and per-pixel start times of the pixels. According to anotherexample, and a two-stage optimization algorithm can be utilized asdescribed in greater detail herein.

In the generated video loop created by the loop construction component106, spatiotemporal neighbors of each pixel can look similar to those inthe input video 102. Because the domain graph is defined on the 2Dspatial grid, the objective function can distinguish both spatial andtemporal consistency:

E _(consistency)(s,p)=βE _(spatial)(s,p)+E _(temporal)(s,p)

The spatial consistency term E_(spatial) can measure compatibility foreach pair of adjacent pixels x and z, averaged over time frames in thevideo loop.

$E_{spatial} = {\sum\limits_{{{x - z}} = 1}\; {\frac{\gamma_{s}\left( {x,z} \right)}{T}{\sum\limits_{t = 0}^{T - 1}\begin{pmatrix}{{{{V\left( {x,{\varphi \left( {x,t} \right)}} \right)} - {V\left( {x,{\varphi \left( {z,t} \right)}} \right)}}}^{2} +} \\{{{V\left( {z,{\varphi \left( {x,t} \right)}} \right)} - {V\left( {z,{\varphi \left( {z,t} \right)}} \right)}}}^{2}\end{pmatrix}}}}$

The period T is the least common multiple (LCM) of the per-pixel loopperiods of the pixels in the input video 102. Accordingly, the objectivecan be formulated as lim_(T→∞) E_(spatial), the average spatialconsistency over an infinitely looping video. Further, pixel valuedifferences at both pixels x and z can be computed for symmetry.Moreover, the factor γ_(s)(x, z) can be as follows:

${\gamma_{s}\left( {x,z} \right)} = \frac{1}{1 + {\lambda_{s}\underset{t}{MAD}{{{V\left( {x,t} \right)} - {V\left( {z,t} \right)}}}}}$

The factor γ_(s)(x, z) can reduce the consistency cost between pixelswhen the temporal median absolute deviation (MAD) of the color values(e.g., differences of color values) in the input video 102 is largebecause inconsistency may be less perceptible. It is contemplated thatMAD can be employed rather than variance due to MAD being less sensitiveto outliers; yet, it is to be appreciated that the claimed subjectmatter is not so limited. Pursuant to another example, the MAD metriccan be defined in terms of respective neighborhoods of the pixel x andthe pixel z, instead of single pixel values V (x, t) and V (z, t).According to a further example, λ_(s) can be set to 100; however, theclaimed subject matter is not so limited.

The energy E_(spatial) (x, z) can be simplified for various scenarios,which can enable efficient evaluation. According to an exemplaryscenario, pixels x and z can both be static. Thus, the energy can reduceto:

E _(spatial)(x,z)=∥V(x,s _(x))−V(x,s _(z))∥² +∥V(z,s _(x))−V(z,s_(z))∥².

In accordance with another exemplary scenario, pixel x can be static andpixel z can be looping. Accordingly, the energy can simplify to:

${E_{spatial}\left( {x,z} \right)} = {\frac{1}{T}{\sum\limits_{t = 0}^{T - 1}\; \begin{pmatrix}{{{{V\left( {x,s_{x}} \right)} - {V\left( {x,{\varphi \left( {z,t} \right)}} \right)}}}^{2} +} \\{{{V\left( {z,s_{x}} \right)} - {V\left( {z,{\varphi \left( {z,t} \right)}} \right)}}}^{2}\end{pmatrix}}}$

For each of the two summed vector norms and for each color coefficientv_(c)εV, the sum can be obtained as:

${\frac{1}{T}{\sum\limits_{t = 0}^{T - 1}\left( {{v_{c}\left( {x,s_{x}} \right)} - {v_{c}\left( {x,{\varphi \left( {z,t} \right)}} \right)}} \right)^{2}}} = {{v_{c}^{2}\left( {x,s_{x}} \right)} - {\frac{2\; {n_{c}\left( {x,s_{x}} \right)}}{p_{z}}{\sum\limits_{t = s_{z}}^{s_{z} + p_{z} - 1}\; {v_{c}\left( {x,t} \right)}}} + {\frac{1}{p_{z}}{\sum\limits_{t = s_{z}}^{s_{z} + p_{z} - 1}{v_{c}^{2}\left( {x,t} \right)}}}}$

The two sums above can be evaluated in constant time by pre-computingtemporal cumulative sum tables on V and V².

In accordance with another exemplary scenario, when both pixels x and zare looping with the same period, p_(z)=p_(z), the energy can reduce to:

${E_{spatial}\left( {x,z} \right)} = {\frac{1}{p_{x}}{\sum\limits_{t = 0}^{p_{x} - 1}\; \begin{pmatrix}{{{{V\left( {x,{\varphi \left( {x,t} \right)}} \right)} - {V\left( {x,{\varphi \left( {z,t} \right)}} \right)}}}^{2} +} \\{{{V\left( {z,{\varphi \left( {x,t} \right)}} \right)} - {V\left( {z,{\varphi \left( {z,t} \right)}} \right)}}}^{2}\end{pmatrix}}}$

Further, the zero value terms for which φ (x, t)=φ (z, t) can bedetected and ignored. Thus, as previously illustrated in FIG. 3, for thecase where start times are similar, significant time intervals markedwith arrows in FIG. 3 can be ignored.

According to another exemplary scenario, when the pixels have differingloop periods, generally the sum is computed using T=LCM(p_(x), p_(z)).However, when the two loop periods are relatively prime (e.g.,LCM(p_(x), p_(z))=p_(x)p_(z)), then the following can be evaluated:

${\frac{1}{mn}{\sum\limits_{i = 0}^{m - 1}\; {\sum\limits_{j = 0}^{n - 1}\; \left( {a_{i} - b_{j}} \right)^{2}}}} = {{\frac{1}{m}{\sum\limits_{i = 0}^{m - 1}a_{i}^{2}}} + {\frac{1}{n}{\sum\limits_{j = 0}^{n - 1}b_{i}^{2}}} - {\frac{2}{mn}\left( {\sum\limits_{i = 0}^{m - 1}a_{i}} \right)\left( {\sum\limits_{j = 0}^{n - 1}b_{i}} \right)}}$

In the foregoing, a and b correspond to coefficients in V (x, ▪) and V(z, ▪). Thus, the recomputed cumulative sum tables from the exemplaryscenario noted above where pixel x is static and pixel z is looping canbe reused to evaluate these terms in constant time.

Moreover, it is contemplated that the expected squared difference can beused as an approximation even when the periods p_(z) and p_(z) are notrelatively prime. Such approximation can provide a speed up withoutappreciably affecting result quality.

Moreover, as noted above, the objective function can include a temporalconsistency objective term E_(temporal).

$E_{temporal} = {\sum\limits_{x}^{\;}\; {\begin{pmatrix}{{{V\left( {x,s_{x}} \right)} - {V\left( {x,{s_{x} + p_{x}}} \right.}^{2} +}} \\{{{V\left( {x,{s_{x} - 1}} \right)} - {V\left( {x,{s_{x} + p_{x} - 1}} \right.}^{2}}}\end{pmatrix}{\gamma_{t}(x)}}}$

The aforementioned temporal consistency objective term can compare, foreach pixel, the value at the per-pixel start time of the loop s_(x) andthe value after the per-pixel end time of the loop s_(x)+p_(x) (e.g.,from a next frame after the per-pixel end time) and, for symmetry, thevalue before the per-pixel start time of the loop s_(x)−1 (e.g., from aprevious frame before the per-pixel start time) and the value at theper-pixel end time of the loop s_(x)+p_(x)−1.

Because looping discontinuities are less perceptible when a pixel variessignificantly over time in the input video 102, the consistency cost canbe attenuated using the following factor:

${\gamma_{t}(x)} = \frac{1}{1 + {\lambda_{t}\underset{t}{{MAD}{{{V\left( {x,t} \right)} - {V\left( {x,{t + 1}} \right)}}}}}}$

The foregoing factor can estimate the temporal variation at the pixelbased on the median absolute deviation of successive pixel differences.According to an example, λ_(t) can be set to 400; yet, the claimedsubject matter is not so limited.

For a pixel assigned as being static (e.g., p_(x)=1), E_(temporal) cancompute the pixel value difference between successive frames, andtherefore, can favor pixels with zero optical flow in the input video102. While such behavior can be reasonable, it may be found that movingobjects can be inhibited from being frozen in a static image. Accordingto another example, it is contemplated that the temporal energy can beset to zero for a pixel assigned to be static.

According to an example, for looping pixels, a factor of

$\frac{1}{p_{x}}$

can be utilized to account for shorter loops revealing temporaldiscontinuities more frequently relative to longer loops. However, it isto be appreciated that the claimed subject matter is not limited toutilization of such factor.

Moreover, the objective function can include a dynamism term thatpenalizes assignment of static loops at the pixels based upon temporalvariances of neighborhoods of the pixels in the input video 102. Forinstance, if the pixels of the input video 102 are each assigned to bestatic from the same input frame, then the loop is spatiotemporallyconsistent without looping. The dynamism term can penalize such trivialsolution and encourage pixels that are dynamic in the input video 102 tobe dynamic in the loop.

A neighborhood N of a pixel can refer to a spatiotemporal neighborhoodof the pixel. Thus, the neighborhood of a given pixel can be a set ofpixels within a specified window in both space and time around the givenpixel, optionally weighted by a kernel (e.g., a Gaussian kernel) thatreduces influence of pixel values that are farther away in space or timefrom the given pixel. Moreover, it is contemplated that the specifiedwindow for a neighborhood of a given pixel can include the given pixelwhile lacking other pixels (e.g., a neighborhood of a given pixel in theinput video 102 can be the given pixel itself).

The dynamism term E_(static) can be utilized to adjust the energyobjective function based on whether the neighborhood N of each pixel hassignificant temporal variance in the input video 102. If a pixel isassigned a static label, it can incur a cost penalty c_(static). Suchpenalty can be reduced according to the temporal variance of theneighborhood N of a pixel. Thus, E_(static)=E_(x|p) _(x) ₌₁ E_(static)(X) can be defined with:

${E_{static}(x)} = {c_{static}{\min \left( {1,\lambda_{static}} \right)}\underset{t}{MAD}{{{N\left( {x,t} \right)} - {N\left( {x,{t + 1}} \right)}}}}$

In the foregoing, λ_(static) can be set to 100, and N can be a Gaussianweighted spatial temporal neighborhood with σ_(x)=0.9 and σ_(t)=1.2.

Now turning to FIG. 4, illustrated is a system 400 that generates avideo loop 402 from the input video 102 using a two-stage optimizationalgorithm. The system 400 includes the reception component 104 thatreceives the input video 102. Moreover, the system 400 includes the loopconstruction component 106 and the optimizer component 108, whichimplement the two-stage optimization algorithm to create the video loop402. Although not shown, it is to be appreciated that the system 400 canfurther include the viewer component 110 of FIG. 1, which can create anoutput video from the video loop 402.

The loop construction component 106 can employ a two-stage approach fordetermining the respective input time intervals for the pixels in theinput video 102. More particularly, the loop construction component 106can include a candidate detection component 404 that forms respectivesets of candidate input time intervals within the time range of theinput video 102 for the pixels in the input video 102. The candidatedetection component 404 can determine respective candidate per-pixelstart times within the time range that optimize and objective functionfor the pixels. The respective candidate per-pixel start times withinthe time range for the pixels can be determined by the candidatedetection component 404 for each candidate loop period. Thus, by way ofillustration, the candidate detection component 404 can identify arespective candidate per-pixel start time for each pixel assuming aper-pixel loop period of 2, a respective candidate per-pixel start timefor each pixel assuming a per-pixel loop period of 3, and so forth.Accordingly, in the first stage, the candidate detection component 404can, for each candidate loop period p>1, find the per-pixel start timess_(x|p) that create an optimized video loop L|p with that candidate loopperiod. The optimizer component 108 can solve a multi-label graph cutfor each candidate loop period, and the candidate detection component404 can identify the respective candidate per-pixel starts times for thepixels based upon results returned by the optimizer component 108 foreach candidate loop period.

Moreover, the loop construction component 106 can include a mergecomponent 406 that can determine the respective input time intervalswithin the time range of the input video 102 for the pixels thatoptimize the objective function. The respective input time intervalswithin the time range of the input video 102 for the pixels can beselected by the merge component 406 from the respective sets ofcandidate input time intervals within the time range of the input video102 for the pixels in the input video 102 as determined by the candidatedetection component 404. Thus, the merge component 406 can determineper-pixel loop periods p_(x)≧1 that define the optimized video loop(p_(x), s_(x|p) _(x) ) using the per-pixel start times obtained by thecandidate detection component 404 (e.g., in the first stage). Theoptimizer component 108 can solve a multi-label graph cut, and the mergecomponent 406 can identify the respective per-pixel loop periods for thepixels based upon results returned by the optimizer component 108. Theset of labels utilized by the optimizer component 108 for the secondstage can include the periods p>1 considered in the first stage togetherwith possible static frames s_(x)′ for the static case p=1. Thus, theoptimization can merge together regions of |{p}|+|{s}| differentcandidate loops: the optimized loops found in the first stage forperiods {p} plus the static loops corresponding to the frames {s} of theinput video 102.

The optimizer component 108 can solve the multi-label graph cuts in bothstages using an iterative alpha-expansion algorithm. The iterativealpha-expansion algorithm can assume a regularity condition on theenergy function, namely that for each pair of adjacent nodes and threelabels α, β, γ, the spatial cost can satisfy c(α, α)+c(β. γ)≦c(α,β)+c(α, γ). Yet, it is to be appreciated that the foregoing constraintmay not be satisfied in the second stage. For instance, the constraintmay not be satisfied when two adjacent pixels are assigned the sameperiod since they may have different per-pixel start times, which canmean that their spatial costs c(α, α) may be nonzero. However, theper-pixel start times can be solved in the first stage to minimize thiscost, so the foregoing difference may likely be mitigated.

Since the regularity condition may not hold, the theoretical boundedapproximation guarantees of the alpha-expansion algorithm may not hold.Yet, some edge costs can be adjusted when setting up eachalpha-expansion pass by the optimizer component 108. More particularly,the optimizer component 108 can add negative costs to the edges c(β, γ),such that the regularity condition is satisfied. Moreover, anotherreason that the energy function may be irregular is that the square rootof E_(spatial)(x, z) can be applied to make it a Euclidean distancerather than a squared distance; yet, the claimed subject matter is notso limited.

Since the iterative multi-label graph cut algorithm may find a localminimum of the objective function, it can be desired to select theinitial state. More particularly for the first stage, the candidatedetection component 404 can initialize s_(x) to minimize temporal cost.Further, for the second stage, the merge component 406 can select p_(x),whose loop L|p_(x) has a minimum spatiotemporal cost at pixel x.

Again, while FIG. 4 describes a two-stage optimization algorithm forgenerating the video loop 402 from the input video 102, it is to beappreciated that alternatively a single-stage optimization algorithm canbe employed to generate the video loop 402 from the input video 102.Thus, following this example, a single-stage multi-label graph cutalgorithm that simultaneously solves for both per-pixel loop periods andper-pixel start times for each pixel can be implemented. While many ofthe examples set forth herein pertain to utilization of the two-stageoptimization algorithm, it is contemplated that such examples can beextended to use of the single-stage optimization algorithm.

Referring now to FIG. 5, illustrated is a system 500 that generates aprogressive video loop spectrum 502 from the input video 102. Similar tothe system 400 of FIG. 4, the two-stage optimization algorithm can beimplemented by the system 500 to form the progressive video loopspectrum 502. The progressive video loop spectrum 502 captures aspectrum of loops from the input video 102 with varying levels ofdynamism, ranging from a static loop to a loop having a maximum level ofdynamism.

The system 500 includes the reception component 104, the loopconstruction component 106, and the optimizer component 108. Again,although not shown, it is contemplated that the system 500 can includethe viewer component 110 of FIG. 1. The loop construction component 106further includes the candidate detection component 404 and the mergecomponent 406 as described herein.

The loop construction component 106 can create the progressive videoloop spectrum 502, represented as

={L_(d)|0≦d≦1}, where d refers to a level of dynamism. The level ofdynamism can be a normalized measure of the temporal variance in a videoloop. At one end of the progressive video loop spectrum 502 is a loop L₀having a minimum level of dynamism (e.g., a static loop). It is to beappreciated that at least two of the pixels in the static loop L₀ can befrom differing frames of the input video 102; yet, the claimed subjectmatter is not so limited. At the other end of the progressive video loopspectrum 502 is a loop L₁ having a maximum level of dynamism, which canhave many of its pixels looping. In the loop L₁, it is to be appreciatedthat some pixels may not be looping (e.g., one or more pixels may bestatic), since forcing pixels with non-loopable content to loop maycause undesirable artifacts.

To define the progressive video loop spectrum 502, each pixel can haveone of two possible states, namely, a static state and a looping state.In the static state, a color value for the pixel can be taken from asingle static frame s_(x)′ of the input video 102. In the looping state,a pixel can have a looping interval [s_(x), s_(x)+p_(x)) that includesthe static frame s_(x)′. Moreover, the loop construction component 106can establish a nesting structure on the set of looping pixels bydefining an activation threshold a_(x)|[0,1) as the level of dynamism atwhich pixel x transitions between static and looping.

Referring to FIG. 6, illustrated are exemplary loops of a progressivevideo loop spectrum (e.g., the progressive video loop spectrum 502 ofFIG. 5). A loop L₁ having a maximum level of dynamism is depicted at600, a static loop L₀ having a minimum level of dynamism is depicted at602, and an intermediate loop having a level of dynamism between theloop L₁ and the loop L₀ is depicted at 604. Looping parameters for thestatic loop L₀ and the subsequent intermediate loops of the progressivevideo loop spectrum are constrained to be compatible with previouslycomputed loops (e.g., looping parameters for the static loop L₀ areconstrained to be compatible with the loop L₁, looping parameters of theintermediate loop depicted at 604 are constrained to be compatible withthe loop L₁ and the loop L₀, etc.).

As shown in the illustrated example, the loop L₁ includes two pixelsthat are static (e.g., in the static state), while the remaining pixelshave respective per-pixel loop periods greater than 1 (e.g., in thelooping state). Moreover, as depicted, the static loop L₀ can include avalue from a static time within an input time interval for each pixel(e.g., each pixel can be in the static state). Further, for theintermediate loop, each pixel can either be in the looping state fromthe loop L₁ or the static state from the static loop L₀.

Again, reference is made to FIG. 5. The loop construction component 106can include a static loop creation component 504 that determines arespective static time for each pixel from the pixels in the input video102 that optimizes an objective function. The respective static time forthe particular pixel can be a single frame selected from within therespective input time interval for the particular pixel (e.g.,determined by the candidate detection component 404 and the mergecomponent 406 as set forth in FIG. 4). Moreover, the respective statictimes for the pixels in the input video 102 can form the static loop L₀.

The loop construction component 106 can further include a thresholdassignment component 506 that assigns a respective activation thresholdfor each pixel from the pixels in the input video 102. The respectiveactivation threshold for the particular pixel can be a level of dynamismat which the particular pixel transitions between static and looping.

A progressively dynamic video loop from the progressive video loopspectrum 502 can have the following time-mapping:

${\varphi_{d}\left( {x,t} \right)} = \left\{ {\begin{matrix}s_{x}^{\prime} & {{{{if}\mspace{14mu} d} \leq a_{x}},} \\{\varphi \left( {x,t} \right)} & {otherwise}\end{matrix}.} \right.$

According to an example, if the level of dynamism d for the loop is lessthan or equal to the activation threshold a_(x) at a given pixel x, thenthe pixel x is static; thus, the output pixel value for the pixel x istaken from a value at the static frame s_(x)′ from the input video 102.Following this example, the output pixel value does not vary as afunction of the output time t. Alternatively, in accordance with anotherexample, if the level of dynamism d for the loop is greater than theactivation threshold a_(x) at the given pixel x, then the pixel x islooping; hence, the output pixel value for the given pixel x isretrieved using the aforementioned time-mapping function φ (x, t), whichwas computed for the loop L₁ having the maximum level of dynamism (e.g.,based upon the per-pixel loop period p_(x) and the per-pixel start times_(x)).

As noted above, the threshold assignment component 506 can determine theactivation threshold a_(x) and the static loop creation component 504can determine the static frames s_(x)′. Moreover, the candidatedetection component 404 and the merge component 406 can determine theper-pixel loop start time s_(x) and the per-pixel loop period p_(x) ateach pixel to provide video loops in the progressive video loop spectrum502

with optimized spatiotemporal consistency. Accordingly, the loopconstruction component 106 can create the progressive video loopspectrum 502 for the input video 102 based upon the respective per-pixelloop periods, the respective per-pixel start times, the respectivestatic times, and the respective activation thresholds for the pixels inthe input video 102. The progressive video loop spectrum 502 can encodea segmentation of the pixels in the input video 102 into independentlylooping spatial regions. For example, a nested segmentation can beencoded; yet, the claimed subject matter is not so limited.

Turning to FIG. 7, illustrated is an exemplary graphical representationof construction of a progressive video loop spectrum implemented by thesystem 500 of FIG. 5. White circles in FIG. 7 denote multi-label graphcuts and black circles in FIG. 7 denote binary graph cuts. Themulti-label graph cuts and the binary graph cuts can be performed by theoptimizer component 108 (or a plurality of optimizer components).

As described above, the two-stage optimization algorithm can be employedto create a loop L₁ 700 having a maximum level of dynamism from theinput video 102. For instance, c_(static) can be set to a value such as10 to form the loop L₁ 700; yet, the claimed subject matter is not solimited.

Subsequent to generation of the loop L₁ 700, a static loop L₀ 702 (e.g.,reference image) can be created (e.g., by the static loop creationcomponent 504 of FIG. 5). The static loop L₀ 702 can be generated byleaving as-is pixels that are static in the loop L₁ 700. Further, staticframes s_(x)′ can be solved for each remaining pixel (e.g., pixels thatare not static in the loop L₁ 700).

Having obtained the parameters (s_(x)′, s_(x), p_(x)), which define thetwo loops {L₀, L₁}⊂

, an activation threshold a_(x) can be assigned at each pixel toestablish the progressive video loop spectrum

. The foregoing can use a recursive binary partition over c_(static)between the loop L₀ 700 and the loop L₁ 702. The threshold assignmentcomponent 506 of FIG. 5 can determine the activation threshold a_(x) ofeach pixel through the set of binary graph cuts (represented by the treeof black circles in FIG. 7).

Again, reference is made to FIG. 5. The progressive video loop spectrum502

can be parameterized using the static cost parameter c_(static), whichcan be varied during construction by the threshold assignment component506. However, the level of activity in a loop often changesnon-uniformly with the static cost parameter c_(static), and suchnon-uniformity can differ significantly across videos. Thus, accordingto another example, the progressive video loop spectrum 502 can beparameterized using a normalized measure of temporal variance within theloop, set forth as follows:

${{Var}(L)} = {\sum\limits_{x}^{\;}\; {\underset{s_{x} \leq t < {s_{x} + p_{x}}}{Var}\left( {V\left( {x,t} \right)} \right)}}$

Var(L) can measure the temporal variance of pixels in a video loop L.The level of dynamism can be defined as the temporal variance normalizedrelative to the loop L₁ having the maximum level of dynamism:

LOD(L)=Var(L)/Var(L ₁)

Thus, as defined, the loop L₁ having a maximum level of dynamism hasLOD(L₁)=1 and the static loop L₀ has LOD(L₀)=0.

The static loop creation component 504 can obtain the static loop L₀ byusing the optimizer component 108 to perform the optimization wherec_(static)=0. Further, the static loop creation component 504 canenforce the constraint s_(x)≦s_(x)′<s_(x)+p_(x) (e.g., as illustrated inFIG. 6). Moreover, the static loop creation component 504 can employ adata term that penalizes color differences from each static pixel to itscorresponding median value in the input video 102. Encouraging medianvalues can help create a static image that represents a still momentfree of transient objects or motions; yet, the claimed subject matter isnot limited to the foregoing example.

Moreover, the threshold assignment component 506 can assign theactivation thresholds as follows. For each pixel x looping in the loopL₁ having the maximum level of dynamism, transitions for such pixelsfrom static to looping occurs between loops L₀ and L₁, and therefore,respective activation thresholds for such pixels satisfy 0≦a_(x)<1.Accordingly, the threshold assignment component 506 forms anintermediate loop by setting

$c_{static} = \frac{\left( {0 + c_{\max}} \right)}{2}$

(e.g., mid-point between the settings for L₀ and L₁) and constrainingeach pixel x to be either static as in the loop L₀ or looping as in theloop L₁. The threshold assignment component 506 can employ the optimizercomponent 108 to minimize E using a binary graph cut. Let d be a levelof dynamism of a resulting loop, and thus, the loop is denoted L_(d).The assignment of each pixel as static or looping in loop L_(d)introduces a further inequality constraint on its activation thresholda_(x) (e.g., either a_(x)<d for looping pixels in L_(d) or a_(x)≧d forstatic pixels in L_(d)). Hence, the threshold assignment component 506can further partition the intervals [L₀, L_(d)] and [L_(d), L₁]recursively to define a_(x) at the pixels of the input video 102.

In the limit of the recursive subdivision, the activation threshold towhich each pixel converges can be a unique value. Recursion canterminate when the change in the static cost parameter c_(static)becomes sufficiently small (e.g., <1.0e−6) or when the differencebetween the level of dynamism of the two loops is sufficiently small(e.g., <0.01). As a post-process, each activation level can be adjustedby the threshold assignment component 506 to lie at a midpoint of avertical step as opposed to at a maximum (or a minimum) of such step;yet, the claimed subject matter is not so limited.

In the progressive video loop spectrum 502, there may be intervals ofdynamism over which the loop does not change. Such discontinuities canexist since the dynamism level is continuous whereas the set of possibleloops is finite A size of some intervals may increase due tospatiotemporal consistency leading to some spatial regions thattransition coherently. Accordingly, some videos can have significantjumps in dynamism. To reduce these jumps, the spatial cost parameter βcan be reduced (e.g., from 10 to 5) by the threshold assignmentcomponent 506 for computation of the activation thresholds; however, theclaimed subject matter is not so limited as such reduction in β may leadto more noticeable spatial seams.

According to another example, the activation threshold for subtle loops(e.g., loops with less activity) can be smaller than the activationthreshold for highly dynamic loops. With E_(static) as defined above,varying c_(static) can have an effect such that when c_(static) is low(e.g., near the static loop L₀), pixels with high temporal variance canbenefit from a greatest drop in E_(static); conversely, when c_(static)is high (e.g., near the loop L₁), pixels with low temporal variance havesufficiently small E_(static) penalties. Thus, loops with higher levelsof activity can transition from static to looping before loops withlower levels of activity (as activation threshold increases). To addressthe foregoing, E_(static) can be redefined for use by the thresholdassignment component 506 (e.g., without being redefined for use by thecandidate detection component 404, the merge component 406, or thestatic loop creation component 504) asE_(static)(x)=c_(static)(1.05−min(1,λ_(static)MAD_(t)|N(x,t)−N(x,t+1)|). Because the loops L₀ and L₁ bounding of therecursive partitioning process are fixed, the effect can be to modifythe activation thresholds and thereby reorder the loops (e.g., withsubtle loops having smaller activation thresholds as compared to moredynamic loops).

According to another example, the respective input time intervals withinthe time range for the pixels of the input video 102 can be determinedat a first spatial resolution (e.g., low spatial resolution). Moreover,the respective input time intervals within the time range for the pixelsof the input video 102 can be up-sampled to a second spatial resolution.Following this example, the second spatial resolution can be greaterthan the first spatial resolution. Thus, the looping parameters computedat the lower resolution can be up-sampled and used for a high resolutioninput video.

Turning to FIG. 8, illustrated is a system 800 that controls renderingof the output video 112. The system 800 includes the viewer component110, which can obtain a source video 802. The source video 802, forexample, can be the input video 102 of FIG. 1. According to anotherexample, the source video 802 can be a compressed input video asdescribed in greater detail herein. Moreover, the viewer component 110can obtain parameters 804 of the progressive video loop spectrum 502generated by the loop construction component 106 of FIG. 5. Theparameters 804 can include, for each pixel, the per-pixel start timess_(x), the per-pixel loop period p_(x), the static time s_(x)′, and theactivation threshold a_(x). By way of another example (where the sourcevideo 802 is the compressed input video), the parameters 804 can beadjusted parameters as set forth in further detail herein.

The viewer component 110 can include a formation component 806 and arender component 808. The formation component 806 can create the outputvideo 112 based upon the source video 802 and the parameters 804. Theparameters 804 can encode a respective input time interval within a timerange of the source video 802 for each pixel in the source video 802.Moreover, a respective input time interval for a particular pixel caninclude a per-pixel loop period of a loop at the particular pixel withinthe time range from the source video 802. The respective input timeinterval for the particular pixel can also include a per-pixel starttime of the loop at the particular pixel within the time range from thesource video 802. Further, the render component 810 can render theoutput video on a display screen of a device.

Further, the viewer component 110 can receive a selection of a level ofdynamism 810 for the output video 112 to be created by the formationcomponent 806 from the source video 802 based upon the parameters 804.According to an example, the selection of the level of dynamism 810 canbe a selection of a global level of dynamism across pixels of the sourcevideo 802. Additionally or alternatively, the selection of the level ofdynamism 810 can be a selection of a local level of dynamism for aportion of the pixels of the source video 802 (e.g., one or more spatialregions). Further, the viewer component 110 can include a dynamismcontrol component 812. More particularly, the dynamism control component812 can control a level of dynamism in the output video 112 based uponthe selection of the level of dynamism 810. Accordingly, the outputvideo 112 can be created by the formation component 806 based uponvalues of the source video 802 and the selection of the level ofdynamism 810 for the output video 112, where the dynamism controlcomponent 812 can control the level of dynamism in the output video 112by causing spatial regions of the output video 112 to respectively beeither static or looping (e.g., a first spatial region can be static orlooping, a second spatial region can be static or looping, etc.).Moreover, the render component 808 can cause the output video 112 to berendered on a display screen of a device.

By way of illustration, the selection of the level of dynamism 810 canbe based upon user input. For instance, a graphical user interface canbe presented to a user, and user input related to content of thegraphical user interface can indicate the selection of the level ofdynamism 810. Yet, it is contemplated that the selection of the level ofdynamism can be obtained from substantially any other source other thanuser input, can be periodically or randomly varied, etc.

According to an example, the level of dynamism in the output video 112can be globally controlled by the dynamism control component 812 acrossthe pixels of the output video 112 based upon the selection of the levelof dynamism 810. By way of illustration, a slider can be included aspart of a graphical user interface rendered on a display screen, wherethe global level of dynamism is controlled based upon position of theslider. As the slider is manipulated to increase the level of dynamism,the output video 112 created by the formation component 806 can becomemore dynamic (e.g., more pixels can transition from static to loopingsuch that the rendered loop becomes more similar to the loop L₁ havingthe maximum level of dynamism). Further, as the slider is manipulated todecrease the level of dynamism, the output video 112 created by theformation component 806 can become less dynamic (e.g., more pixels cantransition from looping to static such that the rendered loop becomesmore similar to the static loop L₀). While utilization of a slider isdescribed in the foregoing example, it is to be appreciated thatsubstantially any type of switch, button, key, etc. included in thegraphical user interface can obtain the selection of the level ofdynamism 810. Further, substantially any other type of interfaces, suchas a natural user interface, can accept the selection of the level ofdynamism 810.

By way of another example, the level of dynamism in the output video 112can be locally controlled within an independently looping spatial regionin the output video 112 based upon the selection of the level ofdynamism 810. For instance, the per-pixel loop periods and activationlevels can induce a segmentation of a scene into independently loopingregions. Thus, rather than using a single path to globally increase ordecrease dynamism globally across the pixels of the output video 112,dynamism can be controlled locally (e.g., based on manual input). Thus,the selection of the level of dynamism 810 can adapt dynamism spatiallyby selectively overriding the looping state per spatial region (e.g., atree included in the source video 802 can be selected to transition fromstatic to looping, etc.).

For fine grain control, it can be desirable for a selectable region tobe small yet sufficiently large to avoid spatial seams when adjacentregions have different states. For instance, two adjacent pixels can bein a common region if the pixels share the same loop period, haverespective input time intervals from the source video 802 that overlap,and have a common activation level. By way of example, a flood-fillalgorithm can find equivalence classes for the transitive closure ofthis relation; yet, the claimed subject matter is not so limited.

By way of illustration, the viewer component 110 can provide a graphicaluser interface to manipulate dynamism over different spatial regions.For instance, as a cursor hovers over the output video 112, a localunderlying region can be highlighted. Other regions can be shaded with acolor coded or otherwise indicated to delineate each region and itscurrent state (e.g., shades of red for static and shades of green forlooping). The selection of the level of dynamism 810 can be based upon amouse click or other selection on a current highlighted spatial region,which can toggle a state of the highlighted spatial region betweenlooping and static. According to another example, dragging a cursor canstart the drawing of a stroke. Following this example, regions thatoverlap the stroke can be activated or deactivated depending on whethera key is pressed (e.g., a shift key). It is to be appreciated, however,that the claimed subject matter is not limited to the foregoingexamples. Also, it is contemplated that other types of interfaces againcan be utilized to accept the selection of the level of dynamism 810.

It is contemplated that the viewer component 110 can receive the inputvideo 102 of FIG. 1 and parameters 804, for example. Additionally oralternatively, the viewer component 110 can receive a compressed inputvideo and adjusted parameters as described in greater detail herein, forexample. Following this example, the output video 112 can be created bythe formation component 806 from the compressed input video based uponthe adjusted parameters (e.g., the level of dynamism can be controlledbased upon the selection of the level of dynamism 810). Thus, the viewercomponent 110 can creates the output video 112 based upon the values atthe pixels over respective input time intervals for the pixels in thecompressed input video.

Turning to FIG. 9, illustrated is a system 900 that compresses the inputvideo 102. The system 900 can include the reception component 104, theloop construction component 106, and the optimizer component 108 asdescribed above. Moreover, although not shown, it is to be appreciatedthat system 900 can include the viewer component 110.

The system 900 further includes a compression component 902. Thecompression component 902 can receive the input video 102 and theparameters of a progressive video loop spectrum (e.g., the progressivevideo loop spectrum 502 of FIG. 5) generated by the loop constructioncomponent 106. The compression component 902 can remap the input video102 based upon the respective input time intervals within the time rangeof the input video 102 for the pixels to form a compressed input video904. The compression component 902 can cause the compressed input video904 to be retained in a data repository 906, for example. Moreover, thecompression component 902 can adjust the parameters of the progressivevideo loop spectrum generated by the loop construction component 106 tocreate adjusted parameters 908. The adjusted parameters 908 can also beretained in the data repository 906, for example.

The progressive video loop spectrum

can include the four per-pixel parameters (s_(x)′, s_(x), p_(x), a_(x)).These per-pixel parameters can have spatial coherence. For instance, theparameters can be stored in to a four channel Portable Networks Graphics(PNG) image with activation thresholds a_(x) quantized to eight bits;however, the claimed subject matter is not so limited.

Because the progressive video loop spectrum

accesses only a subset of the input video 102, the compression component902 can repack contents of the input video 102 into a shorter video V ofmax_(x) p_(x) frames by evaluating the initial frames of the loop L₁having the maximum level of dynamism:

V (x,t)=V(x,φ ₁(x,t)),0≦t<max_(x) p _(x).

Accordingly, the time-mapping function that can be utilized forgenerating the output video from the compressed input video 904 can beas follows:

φ(x,t)=t mod p _(x)

By generating the compressed input video 904, it can be unnecessary tostore per-pixel loop start times s_(x), which can have high entropy andthus may not compress well. The static frames can be adjusted by thecompression component 902 in the adjusted parameters 908 as s _(x)′=φ(x, s_(x)′). Moreover, the compression component 902 can reduce entropyof unused portions of the compressed input video 904 by freezing a lastpixel value of each loop, which aids in compression.

With reference to FIG. 10, illustrated is an input video 1000 (e.g., theinput video 102) and a compressed input video 1002 (e.g., the compressedinput video 904). The compressed input video 1002 includes the portionof the input video 1000. For example, the portion of the input video1000 included in the compressed input video 1002 can be a portion of theinput video 1000 accessed by a loop having a maximum level of dynamism(e.g., the loop L₁) in a progressive video loop spectrum created for theinput video 1000. According to another example, the portion of the inputvideo 1000 included in the compressed input video 1002 can be a portionof the input video 1000 accessed by a single video loop.

Values of pixels in a spatial region 1004 from a corresponding inputtime interval of the input video 1000 can be remapped to the compressedinput video 1002 by evaluating V(x, t)=V(x, φ₁(x, t)). Due to the moduloarithmetic in the time mapping function φ₁(x, t), content of pixels fromthe input video 1000 often can be temporally rearranged when remappedinto the compressed input video 1002. For instance, as depicted in FIG.10, for pixels in the spatial region 1004, some content A appearstemporally prior to other content B in the input video 1000. However,after remapping, the content A is located temporally later than thecontent B in the compressed input video 1002. While the content A andthe content B are rearranged in the compressed input video 10002, it isnoted that the content B still follows the content A when the loopcontent is played (e.g., as it wraps around to form a loop). Similarly,values of pixels in a spatial region 1006, a spatial region 1008, aspatial region 1010, and a spatial region 1012 from respective inputtime intervals of the input video 1000 can be remapped to the compressedinput video 1002.

As illustrated, a per-pixel loop period of the pixels in the spatialregion 1008 can be greater than per-pixel loop periods of the spatialregion 1004, the spatial region 1006, the spatial region 1010 and thespatial region 1012. Thus, a time range of the compressed input video1002 can be the per-pixel loop period for the spatial region 1008.Moreover, a last value for pixels in the spatial region 1004, thespatial region 1006, the spatial region 1010 and the spatial region 1012as remapped in the compressed input video 1002 can be repeated torespectively fill a partial volume 1014, a partial volume 1016, apartial volume 1018, and a partial volume 1020 of the compressed inputvideo 1002.

Various other exemplary aspects generally related to the claimed subjectmatter are described below. It is to be appreciated, however, that theclaimed subject matter is not limited to the following examples.

According to various aspects, in some cases, scene motion or parallaxcan make it difficult to create high-quality looping videos. For thesecases, local alignment of the input video 102 content can be performedto enable enhanced loop creation. Such local alignment can be automaticwithout user input.

In accordance with an example, local alignment can be performed bytreating strong low spatiotemporal frequency edges as structural edgesto be aligned directly, whereas a high spatiotemporal frequency areasare treated as textural regions whose flow is smoothly interpolated. Thevisual result is that aligned structural edges can appear static,leaving the textural regions dynamic and able to be looped. Theforegoing can be achieved utilizing a pyramidal optical flow algorithmwith smoothing to align each frame of the video to a reference videoframe, t_(ref). The reference for the frame can be chosen as the framethat is similar to other frames before local alignment.

To support local alignment, two additional terms can be introduced inthe optimization algorithm described herein. The first term can be asfollows:

${E_{aligned}(x)} = \left\{ {\begin{matrix}\infty & {{{{if}\mspace{14mu} p_{x}} = {{1\mspace{14mu} {and}\mspace{14mu} s_{x}} \neq t_{ref}}}\mspace{11mu}} \\0 & {otherwise}\end{matrix}.} \right.$

E_(aligned)(x) can cause static pixels (e.g., not looping) to be takenfrom the reference frames. The second term can be as follows:

${E_{flow}(x)} = \left\{ {\begin{matrix}0 & {{{{if}\mspace{14mu} p_{x}} = 1},} \\{\lambda_{f\mspace{14mu}}{\max\limits_{s_{x} \leq t < {s_{x} + p_{x}}}{F\left( {x,t} \right)}}} & {otherwise}\end{matrix}.} \right.$

E_(flow) (x) can penalize looping pixels in areas of low confidence foroptical flow, where F (x, t) is the flow reprojection error (computed ata next-to-finest pyramid level) for a pixel x at time t aligned to thereference frame t_(ref). F (x, t) can be set to infinity for pixelswhere the reprojection error is larger than the error before warpingwith the flow field, or where it the warped image is undefined (e.g.,due to out-of-bounds flow vectors). According to an example, μ_(f)=0.3can be used.

The foregoing terms can be employed in the optimization to mitigateloops aligned with poor flow and can cause regions that cannot bealigned to take on values from static reference frame, which can lackalignment error by construction. The looping areas can then be frompixels where the flow error at a course level of the pyramid is low.

In accordance with various aspects, crossfading can be applied to assistmasking spatial and temporal discontinuities in a video loop.Crossfading can be utilized to mitigate blurring due to spatial andtemporal inconsistencies. For instance, temporal crossfading can beperformed during loop creation using a linear blend with an adaptivewindow size that increases linearly with temporal cost of the loop.Spatial crossfading, for example, can be performed at runtime using aspatial Gaussian filter G at a subset of pixels S. The subset of pixelsS can include spatiotemporal pixels with a large spatial cost (e.g.,≧0.003) as well as pixels within an adaptive window size that increaseswith the spatial cost (e.g., up to a 5×5 neighborhood). For each pixelxεS, the following can be computed:

${L\left( {x,t} \right)} = {\sum\limits_{x^{\prime}}^{\;}\; {{G\left( {x^{\prime} - x} \right)}{V\left( {x,{\varphi \left( {x^{\prime},t} \right)}} \right)}}}$

Pursuant to various examples, the foregoing computation of L (x, t) canbe incorporated into the optimization described herein; yet, the claimedsubject matter is not so limited.

FIGS. 11-13 illustrate exemplary methodologies relating generatinglooping video. While the methodologies are shown and described as beinga series of acts that are performed in a sequence, it is to beunderstood and appreciated that the methodologies are not limited by theorder of the sequence. For example, some acts can occur in a differentorder than what is described herein. In addition, an act can occurconcurrently with another act. Further, in some instances, not all actsmay be required to implement a methodology described herein.

Moreover, the acts described herein may be computer-executableinstructions that can be implemented by one or more processors and/orstored on a computer-readable medium or media. The computer-executableinstructions can include a routine, a sub-routine, programs, a thread ofexecution, and/or the like. Still further, results of acts of themethodologies can be stored in a computer-readable medium, displayed ona display device, and/or the like.

FIG. 11 illustrates a methodology 1100 for generating a video loop. At1102, an input video can be received. The input video can include valuesat pixels over a time range. At 1104, an optimization can be performedto determine a respective input time interval within the time range ofthe input video for each pixel from the pixels in the input video. Therespective input time interval for a particular pixel can include aper-pixel loop period and per-pixel start time of the loop at theparticular pixel within the time range from the input video. At 1106, anoutput video can be created based upon the values of the pixels over therespective input time intervals for the pixels in the input video.

Now turning to FIG. 12, illustrated is a methodology 1200 forcompressing an input video. At 1202, an input video can be received. Theinput video can include values at pixels over a time range. At 1204, anoptimization can be performed to create a progressive video loopspectrum for the input video. The progressive video loop spectrum canencode a segmentation of the pixels in the input video intoindependently looping spatial regions. At 1206, the input video can beremapped to form a compressed input video. The compressed input videocan include a portion of the input video accessed by a loop having amaximum level of dynamism in the progressive video loop spectrum.

With reference to FIG. 13, illustrated is a methodology 1300 fordisplaying an output video on a display screen of a device. At 1302, aselection of a level of dynamism for an output video can be received.The level of dynamism can be a normalized measure of temporal variancein a video loop. At 1304, the output video can be created based uponvalues from an input video and the selection of the level of dynamismfor the output video. The level of dynamism in the output video can becontrolled based upon the selection by causing spatial regions of theoutput video to respectively be one of static or looping. At 1306, theoutput video can be rendered on the display screen of the device. Forexample, the output video can be created in real-time per frame asneeded for rendering. Thus, using a time-mapping function as describedherein, values at each pixel can be retrieved from respective input timeintervals in the input video (or a compressed input video).

Referring now to FIG. 14, a high-level illustration of an exemplarycomputing device 1400 that can be used in accordance with the systemsand methodologies disclosed herein is illustrated. For instance, thecomputing device 1400 may be used in a system that forms a video loop ora progressive video loop spectrum from an input video. By way of anotherexample, the computing device 1400 can be used in a system that createsan output video with a controllable level of dynamism. Pursuant to yet afurther example, the computing device 1400 can be used in a system thatcompresses an input video to form a compressed input video. Thecomputing device 1400 includes at least one processor 1402 that executesinstructions that are stored in a memory 1404. The instructions may be,for instance, instructions for implementing functionality described asbeing carried out by one or more components discussed above orinstructions for implementing one or more of the methods describedabove. The processor 1402 may access the memory 1404 by way of a systembus 1406. In addition to storing executable instructions, the memory1404 may also store an input video, parameters associated with aprogressive video loop spectrum, a compressed input video, adjustedparameters, and so forth.

The computing device 1400 additionally includes a data store 1408 thatis accessible by the processor 1402 by way of the system bus 1406. Thedata store 1408 may include executable instructions, an input video,parameters associated with a progressive video loop spectrum, acompressed input video, adjusted parameters, etc. The computing device1400 also includes an input interface 1410 that allows external devicesto communicate with the computing device 1400. For instance, the inputinterface 1410 may be used to receive instructions from an externalcomputer device, from a user, etc. The computing device 1400 alsoincludes an output interface 1412 that interfaces the computing device1400 with one or more external devices. For example, the computingdevice 1400 may display text, images, etc. by way of the outputinterface 1412.

It is contemplated that the external devices that communicate with thecomputing device 1400 via the input interface 1410 and the outputinterface 1412 can be included in an environment that providessubstantially any type of user interface with which a user can interact.Examples of user interface types include graphical user interfaces,natural user interfaces, and so forth. For instance, a graphical userinterface may accept input from a user employing input device(s) such asa keyboard, mouse, remote control, or the like and provide output on anoutput device such as a display. Further, a natural user interface mayenable a user to interact with the computing device 1400 in a mannerfree from constraints imposed by input device such as keyboards, mice,remote controls, and the like. Rather, a natural user interface can relyon speech recognition, touch and stylus recognition, gesture recognitionboth on screen and adjacent to the screen, air gestures, head and eyetracking, voice and speech, vision, touch, gestures, machineintelligence, and so forth.

Additionally, while illustrated as a single system, it is to beunderstood that the computing device 1400 may be a distributed system.Thus, for instance, several devices may be in communication by way of anetwork connection and may collectively perform tasks described as beingperformed by the computing device 1400.

As used herein, the terms “component” and “system” are intended toencompass computer-readable data storage that is configured withcomputer-executable instructions that cause certain functionality to beperformed when executed by a processor. The computer-executableinstructions may include a routine, a function, or the like. It is alsoto be understood that a component or system may be localized on a singledevice or distributed across several devices.

Further, as used herein, the term “exemplary” is intended to mean“serving as an illustration or example of something.”

Various functions described herein can be implemented in hardware,software, or any combination thereof. If implemented in software, thefunctions can be stored on or transmitted over as one or moreinstructions or code on a computer-readable medium. Computer-readablemedia includes computer-readable storage media. A computer-readablestorage media can be any available storage media that can be accessed bya computer. By way of example, and not limitation, suchcomputer-readable storage media can comprise RAM, ROM, EEPROM, CD-ROM orother optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium that can be used to carry or storedesired program code in the form of instructions or data structures andthat can be accessed by a computer. Disk and disc, as used herein,include compact disc (CD), laser disc, optical disc, digital versatiledisc (DVD), floppy disk, and blu-ray disc (BD), where disks usuallyreproduce data magnetically and discs usually reproduce data opticallywith lasers. Further, a propagated signal is not included within thescope of computer-readable storage media. Computer-readable media alsoincludes communication media including any medium that facilitatestransfer of a computer program from one place to another. A connection,for instance, can be a communication medium. For example, if thesoftware is transmitted from a website, server, or other remote sourceusing a coaxial cable, fiber optic cable, twisted pair, digitalsubscriber line (DSL), or wireless technologies such as infrared, radio,and microwave, then the coaxial cable, fiber optic cable, twisted pair,DSL, or wireless technologies such as infrared, radio and microwave areincluded in the definition of communication medium. Combinations of theabove should also be included within the scope of computer-readablemedia.

Alternatively, or in addition, the functionality described herein can beperformed, at least in part, by one or more hardware logic components.For example, and without limitation, illustrative types of hardwarelogic components that can be used include Field-programmable Gate Arrays(FPGAs), Program-specific Integrated Circuits (ASICs), Program-specificStandard Products (ASSPs), System-on-a-chip systems (SOCs), ComplexProgrammable Logic Devices (CPLDs), etc.

What has been described above includes examples of one or moreembodiments. It is, of course, not possible to describe everyconceivable modification and alteration of the above devices ormethodologies for purposes of describing the aforementioned aspects, butone of ordinary skill in the art can recognize that many furthermodifications and permutations of various aspects are possible.Accordingly, the described aspects are intended to embrace all suchalterations, modifications, and variations that fall within the spiritand scope of the appended claims. Furthermore, to the extent that theterm “includes” is used in either the details description or the claims,such term is intended to be inclusive in a manner similar to the term“comprising” as “comprising” is interpreted when employed as atransitional word in a claim.

What is claimed is:
 1. A method of generating a video loop, comprising:receiving an input video, wherein the input video comprises values atpixels over a time range; performing an optimization to determine arespective input time interval within the time range of the input videofor each pixel from the pixels in the input video, wherein therespective input time interval for a particular pixel comprises aper-pixel loop period and a per-pixel start time of a loop at theparticular pixel within the time range from the input video; andcreating an output video based upon the values at the pixels over therespective input time intervals for the pixels in the input video. 2.The method of claim 1, further comprising time-mapping the respectiveinput time interval for the particular pixel in the input video to theoutput video utilizing a modulo-based time-mapping function, wherein anoutput of the modulo-based time-mapping function for the particularpixel is based on the per-pixel loop period and the per-pixel start timeof the loop at the particular pixel from the input video.
 3. The methodof claim 1, wherein performing the optimization further comprisesdetermining the respective input time intervals within the time range ofthe input video for the pixels that optimize an objective function,wherein the objective function comprises a spatial consistency term, atemporal consistency term, and a dynamism term that penalizes assignmentof static loops at the pixels based upon temporal variances ofneighborhoods of the pixels in the input video.
 4. The method of claim1, wherein performing the optimization to determine the respective inputtime interval within the time range of the input video for each pixelfrom the pixels in the input video further comprises: forming respectivesets of candidate input time intervals within the time range of theinput video for the pixels in the input video by determining respectivecandidate per-pixel start times within the time range that optimize anobjective function for the pixels, wherein the respective candidateper-pixel start times within the time range for the pixels aredetermined for each candidate loop period; and determining therespective input time intervals within the time range of the input videofor the pixels that optimize the objective function, wherein therespective input time intervals within the time range of the input videofor the pixels are selected from the respective sets of candidate inputtime intervals within the time range of the input video for the pixelsin the input video.
 5. The method of claim 1, further comprisingdetermining a respective static time for each pixel from the pixels inthe input video that optimize an objective function, wherein therespective static time for the particular pixel is a single frameselected from within the respective input time interval for theparticular pixel, and wherein the respective static times for the pixelsform a static loop.
 6. The method of claim 1, further comprisingassigning a respective activation threshold for each pixel from thepixels in the input video, wherein the respective activation thresholdfor the particular pixel is a level of dynamism at which the particularpixel transitions between static and looping.
 7. The method of claim 1,wherein the per-pixel loop period for the particular pixel is a unityper-pixel loop period, and wherein the particular pixel is static in theoutput video.
 8. The method of claim 1, further comprising: receiving aselection of a level of dynamism for the output video, wherein the levelof dynamism is a normalized measure of temporal variance in a videoloop; and controlling the level of dynamism in the output video basedupon the selection.
 9. The method of claim 8, wherein the level ofdynamism in the output video is globally controlled across the pixels inthe output video based upon the selection.
 10. The method of claim 8,wherein the level of dynamism in the output video is locally controlledwithin an independently looping spatial region in the output video basedupon the selection.
 11. The method of claim 1, further comprisingremapping the input video based upon the respective input time intervalswithin the time range of the input video for the pixels to form acompressed input video, wherein the compressed input video comprises aportion of the input video.
 12. The method of claim 1, wherein therespective input time intervals within the time range for the pixels ofthe input video are determined from the input video at a first spatialresolution, further comprising: upsampling the respective input timeintervals within the time range for the pixels of the input video to asecond spatial resolution, wherein the second spatial resolution isgreater than the first spatial resolution.
 13. The method of claim 1,configured for execution by a processor of a device that captures theinput video.
 14. A system that renders a video loop, comprising: aformation component that creates an output video based upon a sourcevideo and parameters, wherein the parameters encode a respective inputtime interval within a time range of the source video for each pixel inthe source video, and wherein the respective input time interval for aparticular pixel comprises a per-pixel loop period of a loop at theparticular pixel within the time range from the source video; and arender component that renders the output video on a display screen of adevice.
 15. The system of claim 14, wherein the source video is acompressed input video.
 16. The system of claim 14, further comprising adynamism control component that controls a level of dynamism in theoutput video based upon a selection of a level of dynamism.
 17. A methodof displaying an output video on a display screen of a device,comprising: receiving a selection of a level of dynamism for the outputvideo, wherein the level of dynamism is a normalized measure of temporalvariance in a video loop; creating the output video based upon valuesfrom an input video and the selection of the level of dynamism for theoutput video, wherein the level of dynamism in the output video iscontrolled based upon the selection by causing spatial regions of theoutput video to respectively be one of static or looping; and renderingthe output video on the display screen of the device.
 18. The method ofclaim 17, further comprising globally controlling the level of dynamismin the output video based upon the selection.
 19. The method of claim17, further comprising locally controlling the level of dynamism withinan independently looping spatial region in the output video based uponthe selection.
 20. The method of claim 17, wherein the output video iscreated from a compressed input video that is remapped from the inputvideo, wherein the compressed input video comprises a portion of theinput video accessed by a loop having a maximum level of dynamism in aprogressive video loop spectrum created for the input video.